exo-wasm

WASM bindings for EXO-AI 2025 Cognitive Substrate, enabling browser-based deployment of advanced AI substrate operations.

Features

• Pattern Storage: Store and retrieve cognitive patterns with embeddings
• Similarity Search: High-performance vector search with multiple distance metrics
• Temporal Memory: Track patterns with timestamps and causal relationships
• Causal Queries: Query patterns within causal cones
• Browser-First: Optimized for browser deployment with zero-copy transfers

Installation

# Build the WASM package
wasm-pack build --target web

# Or for Node.js
wasm-pack build --target nodejs

Usage

Browser (ES Modules)

import init, { ExoSubstrate, Pattern } from './pkg/exo_wasm.js';

async function main() {
  // Initialize WASM module
  await init();

  // Create substrate
  const substrate = new ExoSubstrate({
    dimensions: 384,
    distance_metric: "cosine",
    use_hnsw: true,
    enable_temporal: true,
    enable_causal: true
  });

  // Create a pattern
  const embedding = new Float32Array(384);
  for (let i = 0; i < 384; i++) {
    embedding[i] = Math.random();
  }

  const pattern = new Pattern(
    embedding,
    { type: "concept", name: "example" },
    [] // antecedents
  );

  // Store pattern
  const id = substrate.store(pattern);
  console.log("Stored pattern:", id);

  // Query for similar patterns
  const results = await substrate.query(embedding, 5);
  console.log("Search results:", results);

  // Get stats
  const stats = substrate.stats();
  console.log("Substrate stats:", stats);
}

main();

Node.js

const { ExoSubstrate, Pattern } = require('./pkg/exo_wasm.js');

const substrate = new ExoSubstrate({
  dimensions: 128,
  distance_metric: "euclidean",
  use_hnsw: false
});

// Use as shown above

API Reference

ExoSubstrate

Main substrate interface.

Constructor

new ExoSubstrate(config)

Config options:

• dimensions (number): Vector dimensions (required)
• distance_metric (string): "euclidean", "cosine", "dotproduct", or "manhattan" (default: "cosine")
• use_hnsw (boolean): Enable HNSW index (default: true)
• enable_temporal (boolean): Enable temporal tracking (default: true)
• enable_causal (boolean): Enable causal tracking (default: true)

Methods

• store(pattern): Store a pattern, returns pattern ID
• query(embedding, k): Search for k similar patterns (returns Promise)
• get(id): Retrieve pattern by ID
• delete(id): Delete pattern by ID
• len(): Get number of patterns
• isEmpty(): Check if substrate is empty
• stats(): Get substrate statistics

Pattern

Represents a cognitive pattern.

Constructor

new Pattern(embedding, metadata, antecedents)

Parameters:

• embedding (Float32Array): Vector embedding
• metadata (object, optional): Arbitrary metadata
• antecedents (string[], optional): IDs of causal antecedents

Properties

• id: Pattern ID (set after storage)
• embedding: Vector embedding (Float32Array)
• metadata: Pattern metadata
• timestamp: Creation timestamp (milliseconds since epoch)
• antecedents: Causal antecedent IDs

Building

Prerequisites

• Rust 1.75+
• wasm-pack
• Node.js (for testing)

Build Commands

# Development build
wasm-pack build --dev

# Production build (optimized)
wasm-pack build --release

# Build for specific target
wasm-pack build --target web      # Browser ES modules
wasm-pack build --target nodejs   # Node.js
wasm-pack build --target bundler  # Webpack/Rollup

Testing

# Run tests in browser
wasm-pack test --headless --firefox

# Run tests in Node.js
wasm-pack test --node

Performance

The WASM bindings are optimized for browser deployment:

• Size: ~2MB gzipped (with SIMD)
• Initialization: <50ms on modern browsers
• Search: 10k+ queries/second (HNSW enabled)
• Zero-copy: Uses transferable objects where possible

Architecture

This crate provides WASM bindings for the EXO-AI 2025 cognitive substrate. It currently uses ruvector-core as the underlying implementation, with plans to integrate with the full EXO substrate layer.

exo-wasm/
├── src/
│   ├── lib.rs      # Main WASM bindings
│   ├── types.rs    # Type conversions
│   └── utils.rs    # Utility functions
├── Cargo.toml
└── README.md

Links

• GitHub
• Website
• EXO-AI Documentation

License

MIT OR Apache-2.0